lazypredict

Lazy Predict

Lazy Predict helps build a lot of basic models without much code and helps understand which models work better without any parameter tuning.

• Free software: MIT license
• Documentation: https://lazypredict.readthedocs.io

Features

• Over 40 built-in machine learning models
• Automatic model selection for classification and regression
• Built-in MLflow integration for experiment tracking
• Support for Python 3.8 through 3.13
• Custom metric evaluation support

Installation

To install Lazy Predict:

pip install lazypredict

Usage

To use Lazy Predict in a project:

import lazypredict

Classification

Example:

from lazypredict.Supervised import LazyClassifier
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split

data = load_breast_cancer()
X = data.data
y = data.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=123)

clf = LazyClassifier(verbose=0, ignore_warnings=True, custom_metric=None)
models, predictions = clf.fit(X_train, X_test, y_train, y_test)

print(models)

Model	Accuracy	Balanced Accuracy	ROC AUC	F1 Score	Time Taken
LinearSVC	0.989474	0.987544	0.987544	0.989462	0.0150008
SGDClassifier	0.989474	0.987544	0.987544	0.989462	0.0109992
MLPClassifier	0.985965	0.986904	0.986904	0.985994	0.426
Perceptron	0.985965	0.984797	0.984797	0.985965	0.0120046
LogisticRegression	0.985965	0.98269	0.98269	0.985934	0.0200036
LogisticRegressionCV	0.985965	0.98269	0.98269	0.985934	0.262997
SVC	0.982456	0.979942	0.979942	0.982437	0.0140011
CalibratedClassifierCV	0.982456	0.975728	0.975728	0.982357	0.0350015
PassiveAggressiveClassifier	0.975439	0.974448	0.974448	0.975464	0.0130005
LabelPropagation	0.975439	0.974448	0.974448	0.975464	0.0429988
LabelSpreading	0.975439	0.974448	0.974448	0.975464	0.0310006
RandomForestClassifier	0.97193	0.969594	0.969594	0.97193	0.033
GradientBoostingClassifier	0.97193	0.967486	0.967486	0.971869	0.166998
QuadraticDiscriminantAnalysis	0.964912	0.966206	0.966206	0.965052	0.0119994
HistGradientBoostingClassifier	0.968421	0.964739	0.964739	0.968387	0.682003
RidgeClassifierCV	0.97193	0.963272	0.963272	0.971736	0.0130029
RidgeClassifier	0.968421	0.960525	0.960525	0.968242	0.0119977
AdaBoostClassifier	0.961404	0.959245	0.959245	0.961444	0.204998
ExtraTreesClassifier	0.961404	0.957138	0.957138	0.961362	0.0270066
KNeighborsClassifier	0.961404	0.95503	0.95503	0.961276	0.0560005
BaggingClassifier	0.947368	0.954577	0.954577	0.947882	0.0559971
BernoulliNB	0.950877	0.951003	0.951003	0.951072	0.0169988
LinearDiscriminantAnalysis	0.961404	0.950816	0.950816	0.961089	0.0199995
GaussianNB	0.954386	0.949536	0.949536	0.954337	0.0139935
NuSVC	0.954386	0.943215	0.943215	0.954014	0.019989
DecisionTreeClassifier	0.936842	0.933693	0.933693	0.936971	0.0170023
NearestCentroid	0.947368	0.933506	0.933506	0.946801	0.0160074
ExtraTreeClassifier	0.922807	0.912168	0.912168	0.922462	0.0109999
CheckingClassifier	0.361404	0.5	0.5	0.191879	0.0170043
DummyClassifier	0.512281	0.489598	0.489598	0.518924	0.0119965

Regression

Example:

from lazypredict.Supervised import LazyRegressor
from sklearn import datasets
from sklearn.utils import shuffle
import numpy as np

diabetes  = datasets.load_diabetes()
X, y = shuffle(diabetes.data, diabetes.target, random_state=13)
X = X.astype(np.float32)

offset = int(X.shape[0] * 0.9)

X_train, y_train = X[:offset], y[:offset]
X_test, y_test = X[offset:], y[offset:]

reg = LazyRegressor(verbose=0, ignore_warnings=False, custom_metric=None)
models, predictions = reg.fit(X_train, X_test, y_train, y_test)

print(models)

Model	Adjusted R-Squared	R-Squared	RMSE	Time Taken
ExtraTreesRegressor	0.378921	0.520076	54.2202	0.121466
OrthogonalMatchingPursuitCV	0.374947	0.517004	54.3934	0.0111742
Lasso	0.373483	0.515873	54.457	0.00620174
LassoLars	0.373474	0.515866	54.4575	0.0087235
LarsCV	0.3715	0.514341	54.5432	0.0160234
LassoCV	0.370413	0.513501	54.5903	0.0624897
PassiveAggressiveRegressor	0.366958	0.510831	54.7399	0.00689793
LassoLarsIC	0.364984	0.509306	54.8252	0.0108321
SGDRegressor	0.364307	0.508783	54.8544	0.0055306
RidgeCV	0.363002	0.507774	54.9107	0.00728202
Ridge	0.363002	0.507774	54.9107	0.00556874
BayesianRidge	0.362296	0.507229	54.9411	0.0122972
LassoLarsCV	0.361749	0.506806	54.9646	0.0175984
TransformedTargetRegressor	0.361749	0.506806	54.9646	0.00604773
LinearRegression	0.361749	0.506806	54.9646	0.00677514
Lars	0.358828	0.504549	55.0903	0.00935149
ElasticNetCV	0.356159	0.502486	55.2048	0.0478678
HuberRegressor	0.355251	0.501785	55.2437	0.0129263
RandomForestRegressor	0.349621	0.497434	55.4844	0.2331
AdaBoostRegressor	0.340416	0.490322	55.8757	0.0512381
LGBMRegressor	0.339239	0.489412	55.9255	0.0396187
HistGradientBoostingRegressor	0.335632	0.486625	56.0779	0.0897055
PoissonRegressor	0.323033	0.476889	56.6072	0.00953603
ElasticNet	0.301755	0.460447	57.4899	0.00604224
KNeighborsRegressor	0.299855	0.458979	57.5681	0.00757337
OrthogonalMatchingPursuit	0.292421	0.453235	57.8729	0.00709486
BaggingRegressor	0.291213	0.452301	57.9223	0.0302746
GradientBoostingRegressor	0.247009	0.418143	59.7011	0.136803
TweedieRegressor	0.244215	0.415984	59.8118	0.00633955
XGBRegressor	0.224263	0.400567	60.5961	0.339694
GammaRegressor	0.223895	0.400283	60.6105	0.0235181
RANSACRegressor	0.203535	0.38455	61.4004	0.0653253
LinearSVR	0.116707	0.317455	64.6607	0.0077076
ExtraTreeRegressor	0.00201902	0.228833	68.7304	0.00626636
NuSVR	-0.0667043	0.175728	71.0575	0.0143399
SVR	-0.0964128	0.152772	72.0402	0.0114729
DummyRegressor	-0.297553	-0.00265478	78.3701	0.00592971
DecisionTreeRegressor	-0.470263	-0.136112	83.4229	0.00749898
GaussianProcessRegressor	-0.769174	-0.367089	91.5109	0.0770502
MLPRegressor	-1.86772	-1.21597	116.508	0.235267
KernelRidge	-5.03822	-3.6659	169.061	0.0243919

MLflow Integration

Lazy Predict includes built-in MLflow integration. Enable it by setting the MLflow tracking URI:

import os
os.environ['MLFLOW_TRACKING_URI'] = 'sqlite:///mlflow.db'

# MLflow tracking will be automatically enabled
reg = LazyRegressor(verbose=0, ignore_warnings=True)
models, predictions = reg.fit(X_train, X_test, y_train, y_test)

Automatically tracks:

• Model metrics (R-squared, RMSE, etc.)
• Training time
• Model parameters
• Model artifacts

title: History

0.2.15 (2025-04-06)

• Added MLflow integration for experiment tracking
• Added support for Python 3.13
• Updated all dependencies to latest versions
• Improved model logging and tracking capabilities
• Added automatic model signature logging with MLflow

0.2.11 (2022-02-06)

• Updated the default version to 3.9

0.2.10 (2022-02-06)

• Fixed issue with older version of Scikit-learn
• Reduced dependencies sctrictly to few

0.2.8 (2021-02-06)

• Removed StackingRegressor and CheckingClassifier.
• Added provided_models method.
• Added adjusted r-squared metric.
• Added cardinality check to split categorical columns into low and high cardinality features.
• Added different transformation pipeline for low and high cardinality features.
• Included all number dtypes as inputs.
• Fixed dependencies.
• Improved documentation.

0.2.7 (2020-07-09)

• Removed catboost regressor and classifier

0.2.6 (2020-01-22)

• Added xgboost, lightgbm, catboost regressors and classifiers

0.2.5 (2020-01-20)

• Removed troublesome regressors from list of CLASSIFIERS

0.2.4 (2020-01-19)

• Removed troublesome regressors from list of REGRESSORS
• Added feature to input custom metric for evaluation
• Added feature to return predictions as dataframe
• Added model training time for each model

0.2.3 (2019-11-22)

• Removed TheilSenRegressor from list of REGRESSORS
• Removed GaussianProcessClassifier from list of CLASSIFIERS

0.2.2 (2019-11-18)

• Fixed automatic deployment issue.

0.2.1 (2019-11-18)

• Release of Regression feature.

0.2.0 (2019-11-17)

• Release of Classification feature.

0.1.0 (2019-11-16)

• First release on PyPI.